Antagonizing binding of cell cycle and apoptosis regulatory protein 1 (CARP-1) to the NEMO/IKKγ protein enhances the anticancer effect of chemotherapy
Bibliographic record
Abstract
NF-κB is a pro-inflammatory transcription factor that critically regulates immune responses and other distinct cellular pathways. However, many NF-κB–mediated pathways for cell survival and apoptosis signaling in cancer remain to be elucidated. Cell cycle and apoptosis regulatory protein 1 (CARP-1 or CCAR1) is a perinuclear phosphoprotein that regulates signaling induced by anticancer chemotherapy and growth factors. Although previous studies have reported that CARP-1 is a part of the NF-κB proteome, regulation of NF-κB signaling by CARP-1 and the molecular mechanism(s) involved are unclear. Here, we report that CARP-1 directly binds the NF-κB–activating kinase IκB kinase subunit γ (NEMO or NF-κB essential modulator) and regulates the chemotherapy-activated canonical NF-κB pathway. Importantly, blockade of NEMO–CARP-1 binding diminished NF-κB activation, indicated by reduced phosphorylation of its subunit p65/RelA by the chemotherapeutic agent adriamycin (ADR), but not NF-κB activation induced by tumor necrosis factor α (TNFα), interleukin (IL)-1β, or epidermal growth factor. High-throughput screening of a chemical library yielded a small molecule inhibitor of NEMO–CARP-1 binding, termed selective NF-κB inhibitor 1 (SNI)-1). We noted that SNI-1 enhances chemotherapy-dependent growth inhibition of a variety of cancer cells, including human triple-negative breast cancer (TNBC) and patient-derived TNBC cells in vitro , and attenuates chemotherapy-induced secretion of the pro-inflammatory cytokines TNFα, IL-1β, and IL-8. SNI-1 also enhanced ADR or cisplatin inhibition of murine TNBC tumors in vivo and reduced systemic levels of pro-inflammatory cytokines. We conclude that inhibition of NEMO–CARP-1 binding enhances responses of cancer cells to chemotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".